Shah Faraaz Ali, Talisa Victor B, Chang Chung-Chou H, Triantafyllou Sofia, Tang Lu, Mayr Florian B, Higgins Alisa M, Peake Sandra L, Mouncey Paul, Harrison David A, DeMerle Kimberley M, Kennedy Jason N, Cooper Gregory F, Bellomo Rinaldo, Rowan Kathy, Yealy Donald M, Seymour Christopher W, Angus Derek C, Yende Sachin P
Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.
Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA.
Crit Care Med. 2025 Jan 1;53(1):e4-e14. doi: 10.1097/CCM.0000000000006463. Epub 2024 Oct 23.
The optimal approach for resuscitation in septic shock remains unclear despite multiple randomized controlled trials (RCTs). Our objective was to investigate whether previously uncharacterized variation across individuals in their response to resuscitation strategies may contribute to conflicting average treatment effects in prior RCTs.
We randomly split study sites from the Australian Resuscitation of Sepsis Evaluation (ARISE) and Protocolized Care for Early Septic Shock (ProCESS) trials into derivation and validation cohorts. We trained machine learning models to predict individual absolute risk differences (iARDs) in 90-day mortality in derivation cohorts and tested for heterogeneity of treatment effect (HTE) in validation cohorts and swapped these cohorts in sensitivity analyses. We fit the best-performing model in a combined dataset to explore roles of patient characteristics and individual components of early goal-directed therapy (EGDT) to determine treatment responses.
Eighty-one sites in Australia, New Zealand, Hong Kong, Finland, Republic of Ireland, and the United States.
Adult patients presenting to the emergency department with severe sepsis or septic shock.
EGDT vs. usual care.
A local-linear random forest model performed best in predicting iARDs. In the validation cohort, HTE was confirmed, evidenced by an interaction between iARD prediction and treatment ( p < 0.001). When patients were grouped based on predicted iARDs, treatment response increased from the lowest to the highest quintiles (absolute risk difference [95% CI], -8% [-19% to 4%] and relative risk reduction, 1.34 [0.89-2.01] in quintile 1 suggesting harm from EGDT, and 12% [1-23%] and 0.64 [0.42-0.96] in quintile 5 suggesting benefit). Sensitivity analyses showed similar findings. Pre-intervention albumin contributed the most to HTE. Analyses of individual EGDT components were inconclusive.
Treatment response to EGDT varied across patients in two multicenter RCTs with large benefits for some patients while others were harmed. Patient characteristics, including albumin, were most important in identifying HTE.
尽管进行了多项随机对照试验(RCT),但脓毒性休克复苏的最佳方法仍不明确。我们的目的是研究个体对复苏策略反应中先前未被描述的差异是否可能导致先前RCT中平均治疗效果相互矛盾。
我们将澳大利亚脓毒症复苏评估(ARISE)试验和早期脓毒性休克程序化治疗(ProCESS)试验中的研究地点随机分为推导队列和验证队列。我们训练机器学习模型来预测推导队列中90天死亡率的个体绝对风险差异(iARDs),并在验证队列中测试治疗效果异质性(HTE),并在敏感性分析中交换这些队列。我们在一个合并数据集中拟合表现最佳的模型,以探索患者特征和早期目标导向治疗(EGDT)各个组成部分的作用,以确定治疗反应。
澳大利亚、新西兰、香港、芬兰、爱尔兰共和国和美国的81个地点。
因严重脓毒症或脓毒性休克就诊于急诊科的成年患者。
EGDT与常规治疗。
局部线性随机森林模型在预测iARDs方面表现最佳。在验证队列中,证实了HTE,iARD预测与治疗之间的相互作用证明了这一点(p<0.001)。当根据预测的iARDs对患者进行分组时,治疗反应从最低五分位数到最高五分位数增加(绝对风险差异[95%CI],-8%[-19%至4%],五分位数1中的相对风险降低为1.34[0.89-2.01],表明EGDT有害,五分位数5中的12%[1-23%]和0.64[0.42-0.96]表明有益)。敏感性分析显示了类似的结果。干预前白蛋白对HTE的贡献最大。对EGDT各个组成部分的分析尚无定论。
在两项多中心RCT中,患者对EGDT的治疗反应各不相同,一些患者受益巨大,而另一些患者则受到伤害。包括白蛋白在内的患者特征在识别HTE方面最为重要。